Client Report - Famous Names

Table of contents

• QUESTION 1
• QUESTION 2

import pandas as pd
import polars as pl
import numpy as np
from lets_plot import *

LetsPlot.setup_html(isolated_frame=True)

# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here
# df_url = pd.read_csv("https://github.com/byuidatascience/data4names/raw/master/data-raw/names_year/names_year.csv")
df = pd.read_csv("names_year.csv")

# df_url_pl = pl.read_csv("https://github.com/byuidatascience/data4names/raw/master/data-raw/names_year/names_year.csv", infer_schema_length=100000)
df_pl = pl.read_csv("names_year.csv", infer_schema_length=100000)

QUESTION 1

Mary, Martha, Peter, and Paul are all Christian names. From 1920 - 2000, compare the name usage of each of the four names in a single chart. What trends do you notice? You must provide a chart. The years labels on your charts should not include a comma.

Mary had two spikes but has consistently declined since the mid-`50’s. Martha gradually declined from not a high point. Peter rose in popularity breifly but has slowly declined since the `60’s

# Q1
# Pandas
df_quad = df.query('name == ["Mary", "Martha", "Peter"]')
quad = (
    ggplot(data=df_quad)
        + geom_line(mapping = aes(x = 'year', y = 'Total', color='name'))
        + scale_x_continuous(limits=[1920,2000], format = 'd')
        + labs(
          title="Frequency of Mary, Martha, Peter, and Paul",
          subtitle="Frequency these names were given to babies in these years.",
          x="Year",
          y="Frequency",
          color="Name"
        )
        # + geom_text(x = 1918, y = 47, label="Spanish Flu", hjust="left")
        # + geom_segment(x = 1945, y = 65, xend = 1960, yend = 60, arrow=arrow(), color = "red")
)
display(quad)

# Polars
print(f"\nUsing Polars")
df_quad_pl = df_pl.filter(pl.col('name').is_in(["Mary", "Martha", "Peter"]))
quad_pl = (
    ggplot(data=df_quad_pl)
        + geom_line(mapping = aes(x = 'year', y = 'Total', color='name'))
        + scale_x_continuous(limits=[1920,2000], format = 'd')
        + labs(
          title="Frequency of Mary, Martha, Peter, and Paul",
          subtitle="Frequency these names were given to babies in these years.",
          x="Year",
          y="Frequency",
          color="Name"
        )
        # + geom_text(x = 1918, y = 47, label="Spanish Flu", hjust="left")
        # + geom_segment(x = 1945, y = 65, xend = 1960, yend = 60, arrow=arrow(), color = "red")
)
display(quad_pl)

Using Polars

QUESTION 2

1. Think of a unique name from a famous movie. Plot the usage of that name and see how changes line up with the movie release. Does it look like the movie had an effect on usage? You must provide a chart. The years labels on your charts should not include a comma.

After the movie 47 Ronin was released a sharp increase in the use of the name ‘Ronin’ is apparent. Following the movie of The Last Samurai a slow increase in the use of the name ‘Ronin’ is present.

# Q2
# Pandas
df_lotr = df.query('name == "Ronin"')
lotr = (
  ggplot(data=df_lotr)
  + geom_line(mapping = aes(x='year', y='Total', color='name'))
  + scale_x_continuous(format = 'd')
  + geom_text(x = 2015, y = 125, label="47 Ronin", hjust="right")
  + geom_segment(x = 2014, y = 150, xend = 2013, yend = 223, arrow=arrow(), color = "blue")
  + geom_text(x = 2000, y = 125, label="The Last Samurai", hjust="left")
  + geom_segment(x = 2002, y = 100, xend = 2003, yend = 26, arrow=arrow(), color = "blue")
  + labs (
    title="Frequency of Ronin",
    subtitle="Frequency this name was given to babies over the years.",
    x='Year',
    y="Frequency",
    color="Name"
  )
)
display(lotr)
# Polars
print(f"Using Polars")
df_lotr_pl = df_pl.filter(pl.col('name').is_in(["Ronin"]))
lotr_pl = (
  ggplot(data=df_lotr_pl)
  + geom_line(mapping = aes(x='year', y='Total', color='name'))
  + scale_x_continuous(format = 'd')
  + geom_text(x = 2015, y = 125, label="47 Ronin", hjust="right")
  + geom_segment(x = 2014, y = 150, xend = 2013, yend = 223, arrow=arrow(), color = "blue")
  + geom_text(x = 2000, y = 125, label="The Last Samurai", hjust="left")
  + geom_segment(x = 2002, y = 100, xend = 2003, yend = 26, arrow=arrow(), color = "blue")
  + labs (
    title="Frequency of Ronin",
    subtitle="Frequency this name was given to babies over the years.",
    x="Year",
    y="Frequency",
    color="Name"
  )
)


display(lotr_pl)

# Additional code for creating a dataframe of just names with columns based on the first initial
uniq_pl = df_pl.sort("name").select("name").unique()
names = uniq_pl.with_columns(pl.col('name').str.slice(0,1).alias("initial"))
grouped = names.group_by("initial").agg(pl.col("name"))
cols = {
  row["initial"]: row["name"]
  for row in grouped.iter_rows(named=True)
}
max_len = max(len(nms) for nms in cols.values())
for key in cols:
  cols[key] += [None] * (max_len - len(cols[key]))

reshaped = pl.DataFrame(cols)
trio = reshaped.select(["A", "X", "Z"])

Using Polars